AI Medical Compendium Topic:
Child, Preschool

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Dual-energy CT based mass density and relative stopping power estimation for proton therapy using physics-informed deep learning.

Physics in medicine and biology
Proton therapy requires accurate dose calculation for treatment planning to ensure the conformal doses are precisely delivered to the targets. The conversion of CT numbers to material properties is a significant source of uncertainty for dose calcula...

Innovating Pedagogical Practices for Handmade Courses in Preschool Education Using Artificial Intelligence.

Computational and mathematical methods in medicine
Handmade is an important part of preschool education, which was aimed at improving children's ability to work with their hands. Preschool education is the most basic and important aspect of a country's educational system. As a result, individuals pur...

Predicting risk of overweight or obesity in Chinese preschool-aged children using artificial intelligence techniques.

Endocrine
OBJECTIVES: We adopted the machine-learning algorithms and deep-learning sequential model to determine and optimize most important factors for overweight and obesity in Chinese preschool-aged children.

Sparking the Interest of Girls in Computer Science via Chemical Experimentation and Robotics: The Qui-Bot HO Case Study.

Sensors (Basel, Switzerland)
We report a new learning approach in science and technology through the Qui-Bot HO project: a multidisciplinary and interdisciplinary project developed with the main objective of inclusively increasing interest in computer science engineering among c...

iCatcher: A neural network approach for automated coding of young children's eye movements.

Infancy : the official journal of the International Society on Infant Studies
Infants' looking behaviors are often used for measuring attention, real-time processing, and learning-often using low-resolution videos. Despite the ubiquity of gaze-related methods in developmental science, current analysis techniques usually involv...

Performance of Deep Learning Models in Forecasting Gait Trajectories of Children with Neurological Disorders.

Sensors (Basel, Switzerland)
Forecasted gait trajectories of children could be used as feedforward input to control lower limb robotic devices, such as exoskeletons and actuated orthotic devices (e.g., Powered Ankle Foot Orthosis-PAFO). Several studies have forecasted healthy ga...

Machine learning model for classification of predominantly allergic and non-allergic asthma among preschool children with asthma hospitalization.

The Journal of asthma : official journal of the Association for the Care of Asthma
OBJECTIVE: Asthma is the most frequent chronic airway illness in preschool children and is difficult to diagnose due to the disease's heterogeneity. This study aimed to investigate different machine learning models and suggested the most effective on...

Artificial intelligence in computed tomography for quantifying lung changes in the era of CFTR modulators.

The European respiratory journal
BACKGROUND: Chest computed tomography (CT) remains the imaging standard for demonstrating cystic fibrosis (CF) airway structural disease . However, visual scoring systems as an outcome measure are time consuming, require training and lack high reprod...

Defining Normal Ranges of Skeletal Muscle Area and Skeletal Muscle Index in Children on CT Using an Automated Deep Learning Pipeline: Implications for Sarcopenia Diagnosis.

AJR. American journal of roentgenology
Skeletal muscle area (SMA), representing skeletal muscle cross-sectional area at the L3 vertebral level, and skeletal muscle index (SMI), representing height-normalized SMA, can serve as markers of sarcopenia. Normal SMA and SMI values have been rep...

Construction of artificial intelligence system of carpal bone age for Chinese children based on China-05 standard.

Medical physics
PURPOSE: The purpose of this study is to construct an automatic carpal bone age evaluation system for Chinese children based on TW3-C Carpal method by deep learning and to evaluate the accuracies in test set and clinical test set.